Predicting hemodynamic parameters based on arterial blood pressure waveform using self-supervised learning and fine-tuning

The arterial blood pressure waveform (ABPW) serves as a less invasive technique for evaluating hemodynamic parameters, offering a lower risk compared to the more invasive pulmonary artery catheter (PAC) thermodilution method. Various studies suggest that deep learning models can potentially predict the hemodynamic parameters of ABPW. However, the scarcity of ground truth data restricts the accuracy of these models, preventing them from gaining clinical acceptance. To mitigate this data and domain challenge, this work proposed a self-supervised generative learning model for hemodynamic parameter prediction, called SSHemo (Self-Supervised Hemodynamic model). Specifically, SSHemo suggests first to leverage large amounts of unlabeled ABPW data to learn the representative embedding and then to fine-tune for the downstream task with a small amount of hemodynamic parameters’ ground truth. To verify the effectiveness of SSHemo, we utilize the public available VitalDB data set to train the model, and evaluation was conducted on two public datasets: VitalDB and MIMIC. The experimental results reveal that SSHemo’s regression mean absolute error (MAE) improved significantly from 1.63 L/min to 1.25 L/min when predicting cardiac output (CO). The trending tracking ability for CO changes meets clinical acceptance (radial limit of agreement (LOA) is $$\pm 25.56$$ °, less than $$\pm 30$$ °). In addition, SSHemo demonstrates robust stability in various conditions and cohorts, as evidenced by subgroup analysis, varying systemic vascular resistance (SVR) range analysis, and rapid CO analysis, compared to the most widely used commercial devices, the EV1000. Computational analysis further underscores the value and potential of practical application of the model in various settings.